Assignment 4 Persuasive or Deceptive Visualization?

Max Katz-Christymaxtkc@mit.edu

Proposition: Secondary education teachers are the key to higher incomes for a country.

FOR the Proposition

Number of secondary teachers compared to GNI

Design Decisions and Rationale:

AGAINST the Proposition

Trained teachers in secondary education compared to GNI

Design Decisions and Rationale:

Final Reflection

I spent most of the time pouring over the different datasets, trying to find some good correlation. I ended up merging education and economics data and using pandas df.corr() method to find any correlating columns. That way I could end up with a scatterplot with nice correlations. It was interesting that I didn't mean to make a misleading plot at first, but ended up with one because the correlation is less affected by the cluster of points with lower GNI and lower population. I think it's easy to trust the numbers and not realize that you need to think about what makes sense, and how the data is assembled. Using raw population, for example, is not great because it doesn't account for country size. Using GNI is probably good, but I'm not an expert on how they calculate it, so their might be issues with it. Sometimes it feels like you are better off using simpler calculations that are more transparent, such as percentage of population, instead of highly processed values like GNI.

I didn't need to do that much with the visualization to make it deceptive. I also didn't really want to, and it would make it seem a lot less trustworthy if the visualization had a bunch of weird suspicious things like adjusted axes. I think having a simpler graph makes it more likely that you can deceive someone because you can hide under the cover of seeming transparency.